We believe that the field of human obesity genetics has stalled. Massive Genome-Wide Association studies of hundreds of thousands of subjects succeeded in identifying over a thousand novel ?statistical loci? that unquestionably tag increased obesity risk variants. Because these loci are almost all in regions that were not on anyone?s candidate gene list, the promise has been that these novel loci point to new biology that could lead to new therapies and prevention strategies. But exactly what these loci do and how they do it continues to remain a mystery for almost all loci. Functional scientists have faced challenges following up on these findings, because of a ?perfect storm? of difficulties. LD patterns in the genome complicate efforts to fine map and statistically dissect driver from passenger variants, even after deep sequencing of the regions in multiple ethnicities, so the exact causal variants are rarely known. Even if they were known, the effect sizes of these variants are each clinically trivial despite their overwhelming statistical significance. Worst of all, it is estimated that over 90% appear to be tagging non-coding regions. We believe a huge rate-limiting step to exploiting these new discoveries continues to be moving from ?statistical loci? to the gene(s) through which they are acting. Many researchers use a default annotation of the ?nearest gene? to the statistical locus as a starting point, but if our published experiments (Baranski et al., 2018) are generalizable, this may be wrong about half the time. Regulome annotation in reference databases is emerging as a critical resource (e.g. ENCODE and GTEX), but it is still early days. The non-coding genome is huge, and much of the annotation is either lacking, insufficient, or not specific enough to allow definitive mapping from locus to gene with these resources alone. Large cohort studies and consortia such as TOPMed have begun conducting various Omics scans in the same individuals in which locus discoveries were made, thus providing the potential to shed light on the underlying mechanisms behind the statistical loci, and in particular, suggest which genes, might be critical. But almost all of these large human efforts are of practical necessity limited to whole blood and tissues of convenience, and much less has been possible in the presumed tissues of action. By contrast, the most important tissues for obesity, like the brain, can be accessed and manipulated in model systems to shed light on mechanisms. Likely every such locus will have a different biological explanation, but pursuing functional experiments for each locus one at a time is challenging, time consuming and expensive. What is needed is a high throughput strategy, that will interrogate many loci simultaneously. We propose to use our successful, published high throughput Drosophila system to efficiently screen for many fat storage genes among the set of human candidates that have fly orthologs (recognizing that this is a ?low hanging fruit? approach that will not work for every locus), and then validate in the mouse those that are conserved from human to insect.